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Hands-On Deep Learning for Games

You're reading from   Hands-On Deep Learning for Games Leverage the power of neural networks and reinforcement learning to build intelligent games

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781788994071
Length 392 pages
Edition 1st Edition
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Author (1):
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Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics
2. Deep Learning for Games FREE CHAPTER 3. Convolutional and Recurrent Networks 4. GAN for Games 5. Building a Deep Learning Gaming Chatbot 6. Section 2: Deep Reinforcement Learning
7. Introducing DRL 8. Unity ML-Agents 9. Agent and the Environment 10. Understanding PPO 11. Rewards and Reinforcement Learning 12. Imitation and Transfer Learning 13. Building Multi-Agent Environments 14. Section 3: Building Games
15. Debugging/Testing a Game with DRL 16. Obstacle Tower Challenge and Beyond 17. Other Books You May Enjoy

Summary

Of all the chapters in this book, this may be the most useful if you are in the process of developing your own game. Game testing is one of those things that requires so much time and attention, it has to be up for some form of automation. While it makes sense for DRL to work well in this area for almost any game, it remains to be seen whether that is one of the niches for this new learning phenomena. One thing that's for sure, however, is that ML-Agents is more than capable of working as a testing harness, and we are sure that it will only get better over time.

In this chapter, we looked at building a generic testing platform, powered by ML-Agents, that we can use to test any game automatically. We first looked at each of the components that we needed to adapt, the academy and the agent, and how they could be generalized for testing. Then, we looked at how we could...

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